Constructing algorithms that efficiently converge to equilibria is one of the central goals of computational game theory. In recent years, regret minimization techniques for approximating equilibria via self play have led to a number of major successes in games like poker [1]}, [2]}, [3]}, [4]}, Avalon [5]}, and Diplomacy [6]}. Regret minimization is now the state-of-the-art approach for computing equilibria in large games, especially those with a large number of actions or in settings where queries to the payoff matrix are too expensive to compute an exact solution via linear programming.
